Overview

Dataset statistics

Number of variables20
Number of observations506
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory108.3 KiB
Average record size in memory219.2 B

Variable types

Categorical2
Numeric18

Warnings

TOWN has a high cardinality: 92 distinct values High cardinality
MEDV is highly correlated with CMEDVHigh correlation
CMEDV is highly correlated with MEDVHigh correlation
RAD is highly correlated with TAXHigh correlation
TAX is highly correlated with RADHigh correlation
TRACT has unique values Unique
ZN has 372 (73.5%) zeros Zeros

Reproduction

Analysis started2021-02-20 09:10:48.510233
Analysis finished2021-02-20 09:12:06.018326
Duration1 minute and 17.51 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

TOWN
Categorical

HIGH CARDINALITY

Distinct92
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
Cambridge
 
30
Boston Savin Hill
 
23
Lynn
 
22
Boston Roxbury
 
19
Newton
 
18
Other values (87)
394 

Length

Max length23
Median length9
Mean length9.9743083
Min length4

Characters and Unicode

Total characters5047
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)3.4%

Sample

1st rowNahant
2nd rowSwampscott
3rd rowSwampscott
4th rowMarblehead
5th rowMarblehead
ValueCountFrequency (%)
Cambridge30
 
5.9%
Boston Savin Hill23
 
4.5%
Lynn22
 
4.3%
Boston Roxbury19
 
3.8%
Newton18
 
3.6%
Somerville15
 
3.0%
Boston South Boston13
 
2.6%
Boston East Boston12
 
2.4%
Brookline12
 
2.4%
Quincy12
 
2.4%
Other values (82)330
65.2%
2021-02-20T10:12:06.888565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
boston157
22.0%
cambridge30
 
4.2%
hill26
 
3.6%
savin23
 
3.2%
roxbury23
 
3.2%
lynn22
 
3.1%
newton18
 
2.5%
somerville15
 
2.1%
south13
 
1.8%
east12
 
1.7%
Other values (87)375
52.5%

Most occurring characters

ValueCountFrequency (%)
o618
 
12.2%
n465
 
9.2%
t389
 
7.7%
e378
 
7.5%
a270
 
5.3%
r264
 
5.2%
s254
 
5.0%
l250
 
5.0%
B220
 
4.4%
i219
 
4.3%
Other values (31)1720
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4109
81.4%
Uppercase Letter722
 
14.3%
Space Separator208
 
4.1%
Dash Punctuation8
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
o618
15.0%
n465
11.3%
t389
9.5%
e378
9.2%
a270
 
6.6%
r264
 
6.4%
s254
 
6.2%
l250
 
6.1%
i219
 
5.3%
d134
 
3.3%
Other values (13)868
21.1%
ValueCountFrequency (%)
B220
30.5%
S75
 
10.4%
W65
 
9.0%
C48
 
6.6%
H44
 
6.1%
M43
 
6.0%
R42
 
5.8%
N41
 
5.7%
L31
 
4.3%
D30
 
4.2%
Other values (6)83
 
11.5%
ValueCountFrequency (%)
208
100.0%
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4831
95.7%
Common216
 
4.3%

Most frequent character per script

ValueCountFrequency (%)
o618
12.8%
n465
 
9.6%
t389
 
8.1%
e378
 
7.8%
a270
 
5.6%
r264
 
5.5%
s254
 
5.3%
l250
 
5.2%
B220
 
4.6%
i219
 
4.5%
Other values (29)1504
31.1%
ValueCountFrequency (%)
208
96.3%
-8
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5047
100.0%

Most frequent character per block

ValueCountFrequency (%)
o618
 
12.2%
n465
 
9.2%
t389
 
7.7%
e378
 
7.5%
a270
 
5.3%
r264
 
5.2%
s254
 
5.0%
l250
 
5.0%
B220
 
4.4%
i219
 
4.3%
Other values (31)1720
34.1%

TOWNNO
Real number (ℝ≥0)

Distinct92
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.53162055
Minimum0
Maximum91
Zeros1
Zeros (%)0.2%
Memory size4.1 KiB
2021-02-20T10:12:07.199746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q126.25
median42
Q378
95-th percentile86.75
Maximum91
Range91
Interquartile range (IQR)51.75

Descriptive statistics

Standard deviation27.57140124
Coefficient of variation (CV)0.580064406
Kurtosis-1.318219483
Mean47.53162055
Median Absolute Deviation (MAD)21.5
Skewness0.03920476703
Sum24051
Variance760.1821665
MonotocityIncreasing
2021-02-20T10:12:07.528134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2830
 
5.9%
8323
 
4.5%
422
 
4.3%
8219
 
3.8%
4018
 
3.6%
2715
 
3.0%
8013
 
2.6%
5912
 
2.4%
4512
 
2.4%
7912
 
2.4%
Other values (82)330
65.2%
ValueCountFrequency (%)
01
 
0.2%
12
 
0.4%
23
 
0.6%
37
 
1.4%
422
4.3%
54
 
0.8%
62
 
0.4%
79
1.8%
84
 
0.8%
91
 
0.2%
ValueCountFrequency (%)
915
 
1.0%
908
 
1.6%
895
 
1.0%
884
 
0.8%
874
 
0.8%
867
 
1.4%
856
 
1.2%
8411
2.2%
8323
4.5%
8219
3.8%

TRACT
Real number (ℝ≥0)

UNIQUE

Distinct506
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2700.357708
Minimum1
Maximum5082
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:07.814321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile430.5
Q11303.25
median3393.5
Q33739.75
95-th percentile4202.75
Maximum5082
Range5081
Interquartile range (IQR)2436.5

Descriptive statistics

Standard deviation1380.03811
Coefficient of variation (CV)0.5110575188
Kurtosis-1.196098004
Mean2700.357708
Median Absolute Deviation (MAD)787
Skewness-0.4358094348
Sum1366381
Variance1904505.185
MonotocityNot monotonic
2021-02-20T10:12:08.091349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.2%
37331
 
0.2%
37461
 
0.2%
37451
 
0.2%
37441
 
0.2%
37431
 
0.2%
37421
 
0.2%
37411
 
0.2%
37401
 
0.2%
37391
 
0.2%
Other values (496)496
98.0%
ValueCountFrequency (%)
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
1011
0.2%
1021
0.2%
ValueCountFrequency (%)
50821
0.2%
50811
0.2%
50711
0.2%
50621
0.2%
50611
0.2%
50521
0.2%
50511
0.2%
50411
0.2%
50311
0.2%
50221
0.2%

LON
Real number (ℝ)

Distinct375
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-71.05638874
Minimum-71.2895
Maximum-70.81
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:08.653667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-71.2895
5-th percentile-71.202375
Q1-71.093225
median-71.0529
Q3-71.019625
95-th percentile-70.936
Maximum-70.81
Range0.4795
Interquartile range (IQR)0.0736

Descriptive statistics

Standard deviation0.07540534773
Coefficient of variation (CV)-0.001061204335
Kurtosis1.108480767
Mean-71.05638874
Median Absolute Deviation (MAD)0.0371
Skewness-0.2053847315
Sum-35954.5327
Variance0.005685966467
MonotocityNot monotonic
2021-02-20T10:12:09.075498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-71.0695
 
1.0%
-71.044
 
0.8%
-71.04554
 
0.8%
-71.024
 
0.8%
-71.0754
 
0.8%
-71.0554
 
0.8%
-71.0594
 
0.8%
-71.034
 
0.8%
-71.0343
 
0.6%
-71.0463
 
0.6%
Other values (365)467
92.3%
ValueCountFrequency (%)
-71.28951
0.2%
-71.28071
0.2%
-71.2691
0.2%
-71.26851
0.2%
-71.2631
0.2%
-71.2621
0.2%
-71.25751
0.2%
-71.2551
0.2%
-71.24751
0.2%
-71.2471
0.2%
ValueCountFrequency (%)
-70.811
0.2%
-70.832
0.4%
-70.8331
0.2%
-70.85251
0.2%
-70.8531
0.2%
-70.8551
0.2%
-70.861
0.2%
-70.88751
0.2%
-70.90751
0.2%
-70.9151
0.2%

LAT
Real number (ℝ≥0)

Distinct376
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.21644032
Minimum42.03
Maximum42.381
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:09.517395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum42.03
5-th percentile42.10745
Q142.180775
median42.2181
Q342.25225
95-th percentile42.31985
Maximum42.381
Range0.351
Interquartile range (IQR)0.071475

Descriptive statistics

Standard deviation0.06177718406
Coefficient of variation (CV)0.001463344223
Kurtosis0.1040024903
Mean42.21644032
Median Absolute Deviation (MAD)0.03625
Skewness-0.08667859819
Sum21361.5188
Variance0.00381642047
MonotocityNot monotonic
2021-02-20T10:12:10.097137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.235
 
1.0%
42.1924
 
0.8%
42.2454
 
0.8%
42.20754
 
0.8%
42.1884
 
0.8%
42.2253
 
0.6%
42.2233
 
0.6%
42.22753
 
0.6%
42.2353
 
0.6%
42.2243
 
0.6%
Other values (366)470
92.9%
ValueCountFrequency (%)
42.031
0.2%
42.04851
0.2%
42.0521
0.2%
42.0592
0.4%
42.06751
0.2%
42.07252
0.4%
42.07351
0.2%
42.07752
0.4%
42.07951
0.2%
42.08251
0.2%
ValueCountFrequency (%)
42.3811
0.2%
42.3741
0.2%
42.37152
0.4%
42.35251
0.2%
42.3462
0.4%
42.3452
0.4%
42.34251
0.2%
42.341
0.2%
42.3391
0.2%
42.33821
0.2%

MEDV
Real number (ℝ≥0)

HIGH CORRELATION

Distinct229
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.53280632
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:10.342178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.197104087
Coefficient of variation (CV)0.408165053
Kurtosis1.495196944
Mean22.53280632
Median Absolute Deviation (MAD)4
Skewness1.108098408
Sum11401.6
Variance84.58672359
MonotocityNot monotonic
2021-02-20T10:12:10.563549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5016
 
3.2%
258
 
1.6%
21.77
 
1.4%
227
 
1.4%
23.17
 
1.4%
20.66
 
1.2%
19.46
 
1.2%
13.85
 
1.0%
22.65
 
1.0%
21.25
 
1.0%
Other values (219)434
85.8%
ValueCountFrequency (%)
52
0.4%
5.61
 
0.2%
6.31
 
0.2%
72
0.4%
7.23
0.6%
7.41
 
0.2%
7.51
 
0.2%
8.11
 
0.2%
8.32
0.4%
8.42
0.4%
ValueCountFrequency (%)
5016
3.2%
48.81
 
0.2%
48.51
 
0.2%
48.31
 
0.2%
46.71
 
0.2%
461
 
0.2%
45.41
 
0.2%
44.81
 
0.2%
441
 
0.2%
43.81
 
0.2%

CMEDV
Real number (ℝ≥0)

HIGH CORRELATION

Distinct228
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.52885375
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:10.832365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.182175882
Coefficient of variation (CV)0.4075740374
Kurtosis1.516783448
Mean22.52885375
Median Absolute Deviation (MAD)4
Skewness1.11091185
Sum11399.6
Variance84.31235393
MonotocityNot monotonic
2021-02-20T10:12:11.112227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5016
 
3.2%
258
 
1.6%
21.77
 
1.4%
23.17
 
1.4%
19.46
 
1.2%
20.66
 
1.2%
226
 
1.2%
17.85
 
1.0%
21.25
 
1.0%
19.35
 
1.0%
Other values (218)435
86.0%
ValueCountFrequency (%)
52
0.4%
5.61
 
0.2%
6.31
 
0.2%
72
0.4%
7.23
0.6%
7.41
 
0.2%
7.51
 
0.2%
8.11
 
0.2%
8.21
 
0.2%
8.32
0.4%
ValueCountFrequency (%)
5016
3.2%
48.81
 
0.2%
48.51
 
0.2%
48.31
 
0.2%
46.71
 
0.2%
461
 
0.2%
45.41
 
0.2%
44.81
 
0.2%
441
 
0.2%
43.81
 
0.2%

CRIM
Real number (ℝ≥0)

Distinct504
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.613523557
Minimum0.00632
Maximum88.9762
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:11.777067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.02791
Q10.082045
median0.25651
Q33.6770825
95-th percentile15.78915
Maximum88.9762
Range88.96988
Interquartile range (IQR)3.5950375

Descriptive statistics

Standard deviation8.601545105
Coefficient of variation (CV)2.380376098
Kurtosis37.13050913
Mean3.613523557
Median Absolute Deviation (MAD)0.22145
Skewness5.223148798
Sum1828.44292
Variance73.9865782
MonotocityNot monotonic
2021-02-20T10:12:12.106077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.015012
 
0.4%
14.33372
 
0.4%
0.578341
 
0.2%
0.061271
 
0.2%
0.035481
 
0.2%
0.14031
 
0.2%
0.037051
 
0.2%
0.955771
 
0.2%
0.117471
 
0.2%
0.035371
 
0.2%
Other values (494)494
97.6%
ValueCountFrequency (%)
0.006321
0.2%
0.009061
0.2%
0.010961
0.2%
0.013011
0.2%
0.013111
0.2%
0.01361
0.2%
0.013811
0.2%
0.014321
0.2%
0.014391
0.2%
0.015012
0.4%
ValueCountFrequency (%)
88.97621
0.2%
73.53411
0.2%
67.92081
0.2%
51.13581
0.2%
45.74611
0.2%
41.52921
0.2%
38.35181
0.2%
37.66191
0.2%
28.65581
0.2%
25.94061
0.2%

ZN
Real number (ℝ≥0)

ZEROS

Distinct26
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.36363636
Minimum0
Maximum100
Zeros372
Zeros (%)73.5%
Memory size4.1 KiB
2021-02-20T10:12:12.327452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.32245299
Coefficient of variation (CV)2.052375864
Kurtosis4.031510084
Mean11.36363636
Median Absolute Deviation (MAD)0
Skewness2.225666323
Sum5750
Variance543.9368137
MonotocityNot monotonic
2021-02-20T10:12:12.652521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0372
73.5%
2021
 
4.2%
8015
 
3.0%
12.510
 
2.0%
2510
 
2.0%
2210
 
2.0%
407
 
1.4%
306
 
1.2%
456
 
1.2%
905
 
1.0%
Other values (16)44
 
8.7%
ValueCountFrequency (%)
0372
73.5%
12.510
 
2.0%
17.51
 
0.2%
181
 
0.2%
2021
 
4.2%
214
 
0.8%
2210
 
2.0%
2510
 
2.0%
283
 
0.6%
306
 
1.2%
ValueCountFrequency (%)
1001
 
0.2%
954
 
0.8%
905
 
1.0%
852
 
0.4%
82.52
 
0.4%
8015
3.0%
753
 
0.6%
703
 
0.6%
604
 
0.8%
553
 
0.6%

INDUS
Real number (ℝ≥0)

Distinct76
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.13677866
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:13.436936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.860352941
Coefficient of variation (CV)0.6160087358
Kurtosis-1.233539601
Mean11.13677866
Median Absolute Deviation (MAD)6.32
Skewness0.2950215679
Sum5635.21
Variance47.06444247
MonotocityNot monotonic
2021-02-20T10:12:13.782030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1132
26.1%
19.5830
 
5.9%
8.1422
 
4.3%
6.218
 
3.6%
21.8915
 
3.0%
9.912
 
2.4%
3.9712
 
2.4%
10.5911
 
2.2%
8.5611
 
2.2%
5.8610
 
2.0%
Other values (66)233
46.0%
ValueCountFrequency (%)
0.461
 
0.2%
0.741
 
0.2%
1.211
 
0.2%
1.221
 
0.2%
1.252
0.4%
1.321
 
0.2%
1.381
 
0.2%
1.472
0.4%
1.524
0.8%
1.692
0.4%
ValueCountFrequency (%)
27.745
 
1.0%
25.657
 
1.4%
21.8915
 
3.0%
19.5830
 
5.9%
18.1132
26.1%
15.043
 
0.6%
13.925
 
1.0%
13.894
 
0.8%
12.836
 
1.2%
11.935
 
1.0%

CHAS
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
471 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters506
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%
2021-02-20T10:12:15.894577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T10:12:16.304313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number506
100.0%

Most frequent character per category

ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common506
100.0%

Most frequent character per script

ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII506
100.0%

Most frequent character per block

ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

NOX
Real number (ℝ≥0)

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5546950593
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:16.468713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40925
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.1158776757
Coefficient of variation (CV)0.2089033853
Kurtosis-0.06466713337
Mean0.5546950593
Median Absolute Deviation (MAD)0.0875
Skewness0.7293079225
Sum280.6757
Variance0.01342763572
MonotocityNot monotonic
2021-02-20T10:12:16.720269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53823
 
4.5%
0.71318
 
3.6%
0.43717
 
3.4%
0.87116
 
3.2%
0.62415
 
3.0%
0.48915
 
3.0%
0.60514
 
2.8%
0.69314
 
2.8%
0.7413
 
2.6%
0.54412
 
2.4%
Other values (71)349
69.0%
ValueCountFrequency (%)
0.3851
 
0.2%
0.3891
 
0.2%
0.3922
0.4%
0.3941
 
0.2%
0.3982
0.4%
0.44
0.8%
0.4013
0.6%
0.4033
0.6%
0.4043
0.6%
0.4053
0.6%
ValueCountFrequency (%)
0.87116
3.2%
0.778
1.6%
0.7413
2.6%
0.7186
 
1.2%
0.71318
3.6%
0.711
2.2%
0.69314
2.8%
0.6798
1.6%
0.6717
 
1.4%
0.6683
 
0.6%

RM
Real number (ℝ≥0)

Distinct446
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.284634387
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:16.919035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.314
Q15.8855
median6.2085
Q36.6235
95-th percentile7.5875
Maximum8.78
Range5.219
Interquartile range (IQR)0.738

Descriptive statistics

Standard deviation0.7026171434
Coefficient of variation (CV)0.1117992074
Kurtosis1.891500366
Mean6.284634387
Median Absolute Deviation (MAD)0.3455
Skewness0.4036121333
Sum3180.025
Variance0.4936708502
MonotocityNot monotonic
2021-02-20T10:12:17.125580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1673
 
0.6%
6.4053
 
0.6%
5.7133
 
0.6%
6.4173
 
0.6%
6.1273
 
0.6%
6.2293
 
0.6%
5.392
 
0.4%
5.3042
 
0.4%
6.9682
 
0.4%
6.0092
 
0.4%
Other values (436)480
94.9%
ValueCountFrequency (%)
3.5611
0.2%
3.8631
0.2%
4.1382
0.4%
4.3681
0.2%
4.5191
0.2%
4.6281
0.2%
4.6521
0.2%
4.881
0.2%
4.9031
0.2%
4.9061
0.2%
ValueCountFrequency (%)
8.781
0.2%
8.7251
0.2%
8.7041
0.2%
8.3981
0.2%
8.3751
0.2%
8.3371
0.2%
8.2971
0.2%
8.2661
0.2%
8.2591
0.2%
8.2471
0.2%

AGE
Real number (ℝ≥0)

Distinct356
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.57490119
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:17.361559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.725
Q145.025
median77.5
Q394.075
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)49.05

Descriptive statistics

Standard deviation28.14886141
Coefficient of variation (CV)0.410483441
Kurtosis-0.9677155942
Mean68.57490119
Median Absolute Deviation (MAD)19.55
Skewness-0.5989626399
Sum34698.9
Variance792.3583985
MonotocityNot monotonic
2021-02-20T10:12:17.963574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10043
 
8.5%
97.94
 
0.8%
964
 
0.8%
95.44
 
0.8%
98.24
 
0.8%
87.94
 
0.8%
98.84
 
0.8%
97.43
 
0.6%
94.13
 
0.6%
96.23
 
0.6%
Other values (346)430
85.0%
ValueCountFrequency (%)
2.91
0.2%
61
0.2%
6.21
0.2%
6.51
0.2%
6.62
0.4%
6.81
0.2%
7.82
0.4%
8.41
0.2%
8.91
0.2%
9.81
0.2%
ValueCountFrequency (%)
10043
8.5%
99.31
 
0.2%
99.11
 
0.2%
98.93
 
0.6%
98.84
 
0.8%
98.71
 
0.2%
98.51
 
0.2%
98.42
 
0.4%
98.32
 
0.4%
98.24
 
0.8%

DIS
Real number (ℝ≥0)

Distinct412
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.795042688
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:18.186587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.461975
Q12.100175
median3.20745
Q35.188425
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.08825

Descriptive statistics

Standard deviation2.105710127
Coefficient of variation (CV)0.5548580872
Kurtosis0.4879411222
Mean3.795042688
Median Absolute Deviation (MAD)1.29115
Skewness1.011780579
Sum1920.2916
Variance4.434015137
MonotocityNot monotonic
2021-02-20T10:12:19.709785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.49525
 
1.0%
5.72094
 
0.8%
5.28734
 
0.8%
6.81474
 
0.8%
5.40074
 
0.8%
7.82783
 
0.6%
3.94543
 
0.6%
7.3093
 
0.6%
5.49173
 
0.6%
6.47983
 
0.6%
Other values (402)470
92.9%
ValueCountFrequency (%)
1.12961
0.2%
1.1371
0.2%
1.16911
0.2%
1.17421
0.2%
1.17811
0.2%
1.20241
0.2%
1.28521
0.2%
1.31631
0.2%
1.32161
0.2%
1.33251
0.2%
ValueCountFrequency (%)
12.12651
0.2%
10.71032
0.4%
10.58572
0.4%
9.22291
0.2%
9.22032
0.4%
9.18761
0.2%
9.08921
0.2%
8.90672
0.4%
8.79212
0.4%
8.69661
0.2%

RAD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.549407115
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:19.881257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.707259384
Coefficient of variation (CV)0.9118115166
Kurtosis-0.8672319936
Mean9.549407115
Median Absolute Deviation (MAD)2
Skewness1.004814648
Sum4832
Variance75.81636598
MonotocityNot monotonic
2021-02-20T10:12:20.214552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24132
26.1%
5115
22.7%
4110
21.7%
338
 
7.5%
626
 
5.1%
224
 
4.7%
824
 
4.7%
120
 
4.0%
717
 
3.4%
ValueCountFrequency (%)
120
 
4.0%
224
 
4.7%
338
 
7.5%
4110
21.7%
5115
22.7%
626
 
5.1%
717
 
3.4%
824
 
4.7%
24132
26.1%
ValueCountFrequency (%)
24132
26.1%
824
 
4.7%
717
 
3.4%
626
 
5.1%
5115
22.7%
4110
21.7%
338
 
7.5%
224
 
4.7%
120
 
4.0%

TAX
Real number (ℝ≥0)

HIGH CORRELATION

Distinct66
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.2371542
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:20.734800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.5371161
Coefficient of variation (CV)0.4128411987
Kurtosis-1.142407992
Mean408.2371542
Median Absolute Deviation (MAD)73
Skewness0.6699559418
Sum206568
Variance28404.75949
MonotocityNot monotonic
2021-02-20T10:12:21.108417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666132
26.1%
30740
 
7.9%
40330
 
5.9%
43715
 
3.0%
30414
 
2.8%
26412
 
2.4%
39812
 
2.4%
38411
 
2.2%
27711
 
2.2%
33010
 
2.0%
Other values (56)219
43.3%
ValueCountFrequency (%)
1871
 
0.2%
1887
1.4%
1938
1.6%
1981
 
0.2%
2165
1.0%
2227
1.4%
2235
1.0%
22410
2.0%
2261
 
0.2%
2339
1.8%
ValueCountFrequency (%)
7115
 
1.0%
666132
26.1%
4691
 
0.2%
43715
 
3.0%
4329
 
1.8%
4303
 
0.6%
4221
 
0.2%
4112
 
0.4%
40330
 
5.9%
4022
 
0.4%

PTRATIO
Real number (ℝ≥0)

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.4555336
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:21.502909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.05
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.164945524
Coefficient of variation (CV)0.1173060379
Kurtosis-0.2850913833
Mean18.4555336
Median Absolute Deviation (MAD)1.15
Skewness-0.8023249269
Sum9338.5
Variance4.686989121
MonotocityNot monotonic
2021-02-20T10:12:21.825660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2140
27.7%
14.734
 
6.7%
2127
 
5.3%
17.823
 
4.5%
19.219
 
3.8%
17.418
 
3.6%
18.617
 
3.4%
19.117
 
3.4%
16.616
 
3.2%
18.416
 
3.2%
Other values (36)179
35.4%
ValueCountFrequency (%)
12.63
 
0.6%
1312
 
2.4%
13.61
 
0.2%
14.41
 
0.2%
14.734
6.7%
14.83
 
0.6%
14.94
 
0.8%
15.11
 
0.2%
15.213
 
2.6%
15.33
 
0.6%
ValueCountFrequency (%)
222
 
0.4%
21.215
 
3.0%
21.11
 
0.2%
2127
 
5.3%
20.911
 
2.2%
20.2140
27.7%
20.15
 
1.0%
19.78
 
1.6%
19.68
 
1.6%
19.219
 
3.8%

B
Real number (ℝ≥0)

Distinct357
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.6740316
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:22.180265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile84.59
Q1375.3775
median391.44
Q3396.225
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)20.8475

Descriptive statistics

Standard deviation91.29486438
Coefficient of variation (CV)0.255961624
Kurtosis7.226817549
Mean356.6740316
Median Absolute Deviation (MAD)5.46
Skewness-2.890373712
Sum180477.06
Variance8334.752263
MonotocityNot monotonic
2021-02-20T10:12:22.701085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9121
 
23.9%
395.243
 
0.6%
393.743
 
0.6%
394.122
 
0.4%
395.562
 
0.4%
390.942
 
0.4%
388.452
 
0.4%
393.232
 
0.4%
396.212
 
0.4%
393.372
 
0.4%
Other values (347)365
72.1%
ValueCountFrequency (%)
0.321
0.2%
2.521
0.2%
2.61
0.2%
3.51
0.2%
3.651
0.2%
6.681
0.2%
7.681
0.2%
9.321
0.2%
10.481
0.2%
16.451
0.2%
ValueCountFrequency (%)
396.9121
23.9%
396.421
 
0.2%
396.331
 
0.2%
396.31
 
0.2%
396.281
 
0.2%
396.241
 
0.2%
396.231
 
0.2%
396.212
 
0.4%
396.141
 
0.2%
396.062
 
0.4%

LSTAT
Real number (ℝ≥0)

Distinct455
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.65306324
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-02-20T10:12:25.756495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.7075
Q16.95
median11.36
Q316.955
95-th percentile26.8075
Maximum37.97
Range36.24
Interquartile range (IQR)10.005

Descriptive statistics

Standard deviation7.141061511
Coefficient of variation (CV)0.5643741263
Kurtosis0.4932395174
Mean12.65306324
Median Absolute Deviation (MAD)4.795
Skewness0.9064600936
Sum6402.45
Variance50.99475951
MonotocityNot monotonic
2021-02-20T10:12:27.715106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.053
 
0.6%
6.363
 
0.6%
18.133
 
0.6%
14.13
 
0.6%
7.793
 
0.6%
18.462
 
0.4%
9.972
 
0.4%
5.332
 
0.4%
10.452
 
0.4%
6.722
 
0.4%
Other values (445)481
95.1%
ValueCountFrequency (%)
1.731
0.2%
1.921
0.2%
1.981
0.2%
2.471
0.2%
2.871
0.2%
2.881
0.2%
2.941
0.2%
2.961
0.2%
2.971
0.2%
2.981
0.2%
ValueCountFrequency (%)
37.971
0.2%
36.981
0.2%
34.771
0.2%
34.411
0.2%
34.371
0.2%
34.021
0.2%
31.991
0.2%
30.812
0.4%
30.631
0.2%
30.621
0.2%

Interactions

2021-02-20T10:10:51.021390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:51.164707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:51.290901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:51.421274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:51.547802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:51.683186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:51.813615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:51.962855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:52.082634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:52.205645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:52.355783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:52.482532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:52.625931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:52.773416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:52.917033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:53.048870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:53.182381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:10:53.317024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-20T10:11:36.330448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:36.496912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:36.680228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:36.839266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:37.045268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:37.260864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:37.436112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:37.801151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:37.982807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:38.130607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:38.268992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:38.414409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:38.570192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:38.694951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:38.837115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:38.982091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:39.125360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:39.267681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:39.417164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:39.649077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:39.873456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:43.645433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:44.030375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:44.495868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:45.593232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:47.138956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:48.115311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:48.786580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:49.324754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:50.499887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:50.786932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:51.032084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:51.247416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:51.670427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:52.126928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:52.674865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:52.968686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:53.278946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:53.531045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:53.959906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:54.420527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:54.612073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:54.788972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:54.984997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:55.183993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:55.314894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:55.440865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:55.569193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:55.728262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:55.937827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:56.286327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:56.444820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:56.651328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:56.877784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:57.099324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:57.404706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:57.555526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:57.748316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:57.895713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:58.046904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:58.205470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:58.415803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:58.574698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:58.720919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:58.901376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:59.143797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:59.324088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:59.548340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:59.717227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:11:59.869224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:00.512170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:00.713198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:00.853799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:01.017549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:01.163318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:01.299515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:01.508051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:01.736188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:01.941620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:02.116631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:02.772512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:03.226192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:03.390000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:03.530951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:03.671634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:03.831686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T10:12:03.997738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-20T10:12:31.303142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-20T10:12:34.041652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-20T10:12:35.780257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-20T10:12:36.914471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-20T10:12:37.980106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-20T10:12:04.383726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-20T10:12:05.827341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TOWNTOWNNOTRACTLONLATMEDVCMEDVCRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
0Nahant02011-70.955042.255024.024.00.0063218.02.3100.5386.57565.24.0900129615.3396.904.98
1Swampscott12021-70.950042.287521.621.60.027310.07.0700.4696.42178.94.9671224217.8396.909.14
2Swampscott12022-70.936042.283034.734.70.027290.07.0700.4697.18561.14.9671224217.8392.834.03
3Marblehead22031-70.928042.293033.433.40.032370.02.1800.4586.99845.86.0622322218.7394.632.94
4Marblehead22032-70.922042.298036.236.20.069050.02.1800.4587.14754.26.0622322218.7396.905.33
5Marblehead22033-70.916542.304028.728.70.029850.02.1800.4586.43058.76.0622322218.7394.125.21
6Salem32041-70.936042.297022.922.90.0882912.57.8700.5246.01266.65.5605531115.2395.6012.43
7Salem32042-70.937542.310027.122.10.1445512.57.8700.5246.17296.15.9505531115.2396.9019.15
8Salem32043-70.933042.312016.516.50.2112412.57.8700.5245.631100.06.0821531115.2386.6329.93
9Salem32044-70.929042.316018.918.90.1700412.57.8700.5246.00485.96.5921531115.2386.7117.10

Last rows

TOWNTOWNNOTRACTLONLATMEDVCMEDVCRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
496Revere901704-71.001042.252519.719.70.289600.09.6900.5855.39072.92.7986639119.2396.9021.14
497Revere901705-70.994742.249618.318.30.268380.09.6900.5855.79470.62.8927639119.2396.9014.10
498Revere901706-71.005042.245521.221.20.239120.09.6900.5856.01965.32.4091639119.2396.9012.92
499Revere901707-70.998542.243017.517.50.177830.09.6900.5855.56973.52.3999639119.2395.7715.10
500Revere901708-70.992042.238016.816.80.224380.09.6900.5856.02779.72.4982639119.2396.9014.33
501Winthrop911801-70.986042.231222.422.40.062630.011.9300.5736.59369.12.4786127321.0391.999.67
502Winthrop911802-70.991042.227520.620.60.045270.011.9300.5736.12076.72.2875127321.0396.909.08
503Winthrop911803-70.994842.226023.923.90.060760.011.9300.5736.97691.02.1675127321.0396.905.64
504Winthrop911804-70.987542.224022.022.00.109590.011.9300.5736.79489.32.3889127321.0393.456.48
505Winthrop911805-70.982542.221011.919.00.047410.011.9300.5736.03080.82.5050127321.0396.907.88